LWCal: Loss-Weighted Calibration for Tabular Classifiers with Noisy Calibration Labels
Post-hoc probability calibration is usually evaluated under an optimistic assumption: the held-out calibration labels are clean. In many AI deployment settings, however, labels come from weak annotators, historical decisions, heuristics, or distant supervision, so the same label noise that corrupts training also corrup...
Ze-Ming Liu, Hang Lyu, Jing-Tao Zhang et al.
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